Methods to Estimate Carbon Dioxide Emissions Reduction and Low-Stress Bicycle Accessibility for School-Related Trips: a Montreal Case Study
Bibliographic record
Abstract
Despite the important benefits, urban cycling still faces multiple barriers, mainly the lack of bicycle infrastructure, road safety, and adverse weather conditions. Cycling can be a relevant mode for short daily trips, such as school-related trips for children and teenagers. However, in part due to such barriers, the percentage of bicycle school trips is still very low in Canadian cities, including the City of Montreal. The thesis has a two-fold objective: 1) to estimate the potential CO2 savings of bicycle school trips, and 2) to evaluate the bicycleinfrastructure needs to improve the accessibility around schools using the Level of Traffic Stress method.For the first objective, a link-level emission model is calibrated for the City of Montreal using real-world CO2 measurements. Then, Montreal origin-destination data is analyzed to identify the school trips that could be transferred from private vehicles to bicycle trips. Afterward, the calibrated link-level emission model is applied to those transferable trips to estimate the CO2 emissions savings. For the second objective, we assess the biking low-stress accessibility to schools in Montreal. To achieve this, we applied the Level of Traffic Stress(LTS) methodology and developed an accessibility index for each school. This metric is then used to evaluate and prioritize potential biking network infrastructure improvements. Additionally, low-detour criteria or multi-modal trips are investigated to estimate their impact on school accessibility.This research shows that a realistic CO2 emission factor for an average Quebec light-duty vehicle is 285 gCO2/km, 42% more than the value of 200 gCO2/km observed in the literature. In the region of Montreal, it was found that 15% of the school-related car trips could be replaced by cycling trips given their proximity to schools (less than 4.5 kilometers). This represents 4.7 million kilometers traveled or 1335 tons of CO2 that could be avoided yearly for the region of Montreal. However, based on the Level of Traffic Stress analysis of the current bicycle network of Montreal, 94% of the schools have an accessibility below 10% and 49% have a null accessibility, which likely deters or prevents many children to cycle to school. A scenario of local improvements within 200 meters of the schools is evaluated, and the percentage of schools having a null accessibility decreases to 23%. The findings of this research can help city planners to identify the CO2 savings from bicycle use and advocate for its widespread use, and to evaluate the current accessibility around schools and select network improvement projects that increase children’s access to schools by bicycle the most
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".